Location: Soil and Water Management ResearchTitle: Soil heat flux calculation for sunlit and shaded surfaces under row crops: 2. Model Test Author
|Evett, Steven - Steve|
|Kustas, William - Bill|
Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/11/2015
Publication Date: 11/10/2015
Publication URL: http://handle.nal.usda.gov/10113/61947
Citation: Colaizzi, P.D., Evett, S.R., Schwartz, R.C., Kustas, W.P., Cosh, M.H., Mckee, L.G. 2015. Soil heat flux calculation for sunlit and shaded surfaces under row crops: 2. Model Test. Agricultural and Forest Meteorology. 216(129):129-140. doi:10.1016/j.agrformet.2015.10.009.
Interpretive Summary: Irrigated crops consume a large portion of freshwater resources, but irrigation results in up to four times greater crop production compared with non-irrigated crops. It is therefore important to manage irrigation water in order to maintain or increase crop production for a growing world population while conserving freshwater resources for municipal, industrial, environmental, and recreational uses. Management of irrigation water requires knowledge of crop water use; however, crop water use is related to numerous complex factors. One important factor is how much the soil beneath a crop is sunlit or shaded. Sun lighting and shading of the soil change with time of day, type of crop, crop row direction, and crop growth stage, among other factors. Scientists from ARS laboratories in Bushland, TX and Beltsville, MD, and Ben-Gurion University of the Negev, Israel developed and tested a new mathematical model to calculate soil sun lighting and shading beneath a row crop. The new model resulted in improved estimates of factors related to crop water use. The use of this model will improve irrigation water management and conserve freshwater resources.
Technical Abstract: A method to calculate surface soil heat flux (G0) as a function of net radiation to the soil (RN,S) was developed that accounts for positional variability across a row crop interrow. The method divides the interrow into separate sections, which may be shaded, partially sunlit, or fully sunlit, and calculates RN,S for each interrow section using a relatively simple geometric approach. Normalized RN,S is then related to normalized G0 for 24 h time steps through a single empirical parameter derived in Part 1. The method was tested against G0 derived from the calorimetric method for upland cotton (Gossypium hirsutum L.) with north-south (NS) and east-west (EW) row orientations from sparse to full canopy cover at Bushland, Texas, USA. Data were grouped by canopy cover for three periods in the growing season, including sparse (BEG), medium (MID), and full (END). For each row orientation, measurements used for calorimetric G0 were located at five interrow positions in two replicates; one position was used for model calibration, and four positions were used for the model test. For NS, soil temperature and volumetric soil water content at 0.02 and 0.06 m depths and soil heat flux at the 0.08 m depth below the surface were measured. For EW, soil temperature and soil heat flux were measured at the same depths and positions as for NS, but volumetric water content was obtained only at a single depth (0.05 m) and in the interrow center in three replicates. Discrepancy between calculated and calorimetric G0 was larger for EW compared with NS rows for BEG and MID periods (partial canopy cover), but nearly the same during the END period (full canopy cover). During BEG and MID, the greater discrepancy of calorimetric G0 vs. calculated G0 for EW rows compared with NS may have been related to measurement of volumetric soil water at only a single depth and interrow position, as well as lower sensor accuracy, compared with NS rows. For NS, the Nash-Sutcliffe modified Index of Agreement (IOA) was 0.81 to 0.84; for EW, it was 0.69 to 0.78 throughout the growing season. The method provided a straightforward way to account for positional variability of G0 across a row crop interrow, which was most important for NS rows during sparse to medium canopy cover.